
"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a       :						List[Any]       		=		{
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						Any       		=		['''MobileNetV2FeatureExtractor''']
 a       :						Union[str, Any]       		=		['''MobileNetV2ImageProcessor''']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						Any       		=		[
     '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileNetV2ForImageClassification''',
     '''MobileNetV2ForSemanticSegmentation''',
     '''MobileNetV2Model''',
     '''MobileNetV2PreTrainedModel''',
     '''load_tf_weights_in_mobilenet_v2''',
 ]


if TYPE_CHECKING:
 from .configuration_mobilenet_va import (
     MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileNetVaConfig,
     MobileNetVaOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilenet_va import (
      MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileNetVaForImageClassification,
      MobileNetVaForSemanticSegmentation,
      MobileNetVaModel,
      MobileNetVaPreTrainedModel,
      load_tf_weights_in_mobilenet_va,
  )


else:
 import sys

 a       :						str       		=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)